Evolutionary extreme learning machine for the interval type-2 radial basis function neural network

A fuzzy modelling approach

Adrian Rubio-Solis, Uriel Martinez-Hernandez, George Panoutsos

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Evolutionary Extreme Learning Machine (E-ELM) is frequently much more efficient than traditional gradient-based algorithms for the parameter identification of feedforward neural networks. In particular, E-ELM is usually faster and provides a higher trade-off between accuracy and model simplicity. For that reason, this paper shows that an E-ELM that is based on Particle Swarm Optimisation (PSO) and Extreme Learning machine (ELM) can be extended to the Interval Type-2 Radial Basis Function Neural Network (IT2-RBFNN) with a Karnik-Mendel type-reduction layer. To evaluate the efficiency of E-ELM, the IT2-RBFNN is used as an Interval Type-2 Fuzzy Logic System (IT2 FLS) for the modelling of two popular benchmark data sets and for the prediction of chaotic time series. According to our results, E-ELM applied to the IT2-RBFNN not only outperforms adaptive-gradient-based algorithms and provides a better generalisation compared to other existing IT2 fuzzy methodologies, but similarly to pure fuzzy models, the IT2-RBFNN is also able to preserve some model interpretation and transparency.

Original languageEnglish
Title of host publication2018 IEEE International Conference on Fuzzy Systems, FUZZ 2018 - Proceedings
PublisherIEEE
Number of pages3
Volume2018-July
ISBN (Electronic)9781509060207
DOIs
Publication statusPublished - 12 Oct 2018
Event2018 IEEE International Conference on Fuzzy Systems, FUZZ 2018 - Rio de Janeiro, Brazil
Duration: 8 Jul 201813 Jul 2018

Conference

Conference2018 IEEE International Conference on Fuzzy Systems, FUZZ 2018
CountryBrazil
CityRio de Janeiro
Period8/07/1813/07/18

Keywords

  • Extreme learning machine
  • Fuzzy modelling
  • Interval type-2 fuzzy logic systems. RBF neural networks
  • Particle Swarm Optimisation (PSO)

ASJC Scopus subject areas

  • Software
  • Theoretical Computer Science
  • Artificial Intelligence
  • Applied Mathematics

Cite this

Rubio-Solis, A., Martinez-Hernandez, U., & Panoutsos, G. (2018). Evolutionary extreme learning machine for the interval type-2 radial basis function neural network: A fuzzy modelling approach. In 2018 IEEE International Conference on Fuzzy Systems, FUZZ 2018 - Proceedings (Vol. 2018-July). [8491583] IEEE. https://doi.org/10.1109/FUZZ-IEEE.2018.8491583

Evolutionary extreme learning machine for the interval type-2 radial basis function neural network : A fuzzy modelling approach. / Rubio-Solis, Adrian; Martinez-Hernandez, Uriel; Panoutsos, George.

2018 IEEE International Conference on Fuzzy Systems, FUZZ 2018 - Proceedings. Vol. 2018-July IEEE, 2018. 8491583.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Rubio-Solis, A, Martinez-Hernandez, U & Panoutsos, G 2018, Evolutionary extreme learning machine for the interval type-2 radial basis function neural network: A fuzzy modelling approach. in 2018 IEEE International Conference on Fuzzy Systems, FUZZ 2018 - Proceedings. vol. 2018-July, 8491583, IEEE, 2018 IEEE International Conference on Fuzzy Systems, FUZZ 2018, Rio de Janeiro, Brazil, 8/07/18. https://doi.org/10.1109/FUZZ-IEEE.2018.8491583
Rubio-Solis A, Martinez-Hernandez U, Panoutsos G. Evolutionary extreme learning machine for the interval type-2 radial basis function neural network: A fuzzy modelling approach. In 2018 IEEE International Conference on Fuzzy Systems, FUZZ 2018 - Proceedings. Vol. 2018-July. IEEE. 2018. 8491583 https://doi.org/10.1109/FUZZ-IEEE.2018.8491583
Rubio-Solis, Adrian ; Martinez-Hernandez, Uriel ; Panoutsos, George. / Evolutionary extreme learning machine for the interval type-2 radial basis function neural network : A fuzzy modelling approach. 2018 IEEE International Conference on Fuzzy Systems, FUZZ 2018 - Proceedings. Vol. 2018-July IEEE, 2018.
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